基于遗传算法的动态聚类方法

针对常规动态聚类方法对初始聚类中心的敏感性以及聚类结果与样本输入次序有关等问题，本文另辟蹊径，提出了一种基于ＧＡ 的动态聚类方法，并将它应用到数据库的数据分析中． 计算结果表明，该方法是一个具有全局最优解的动态聚类方法，其结果明显好于Ｋ－均值聚类算法．

A Dynamic Clustering Method Based on Genetic Algorithms

To solve the problem of sensitivity with the original clustering center and clustering results depended on the order of the input example in common dynamic clustering algorithm, a new dynamic clustering method based on genetic algorithms is presented in this paper and is applied to data analysis in databases. Computing results indicate that the method is a dynamic clustering algorithm with global optimization and is superior to $K|$ means algorithm.